MLOps offers deployment that is totally agnostic. You pick which platform you want to deploy on. You pick which frameworks or languages you want to use.
Monitoring models is essential to ensuring that they are continually producing value. MLOps gives you a system for monitoring all your models, no matter where they are deployed or what frameworks you used to build the models.
Your models will need to be updated. Manual updates are time-consuming and problematic. Lifecycle management makes it easier for data scientists to manage a large portfolio of production models.
Deployment is just the start. It’s also important to have in place robust governance practices, review processes, and tools to minimize risk and ensure regulatory compliance.
DataRobot MLOps allows data science leaders and teams to embed cutting edge predictive models in an efficient and value-driven way no matter what. From agents to being cloud agnostic, MLOps is flexible.
See What MLOps Can Do for Data Scientists
Three Key Feature Sets
Unleash the ability to work with different types and shapes of data that serve your needs.
- Real-time predictions
- Batch predictions
- Service health monitoring
- Time series predictions
- Image and geospatial data types
- Java scoring code
- Portable docker image
Operating at Scale
Use and build upon the foundation you already have.
- Monitoring diverse prediction environments
- Audit logs
- Versioning and lineage
- Change approval workflows
- No-code prediction GUI
- Value and use case tracking
- Repo integration
Making ML Trustworthy
Deploy reliable, trustworthy, and unbiased models.
- Data drift analysis
- Accuracy analysis
- Anomaly warnings
- Prediction explanations
- Champion/Challenger gates into production
- Humble AI – built in mechanisms ensuring trust in your models
- Prediction intervals
The Only Scalable MLOps Architecture
I really think using DataRobot MLOps is the reason why we didn’t have to stress about it [COVID] as much as other companies have. The only reason we were comfortable in doing that is that when we see performance changes via MLOps we can throw everything automatically back into DataRobot AutoML and see what it tells us in terms of model comparison and see what we need to do based on where we’re at at that point of time.
DataRobot not only helped us to reduce overhiring by 60%, but we were even able to increase sales by an unknown amount by rectifying underhiring, fulfilling more orders in our fulfillment centers.
DataRobot has helped our data science team to drastically accelerate our work. What would previously have taken us two-and-a-half weeks can now be done in hours. It’s like my group of 10 is really a group of 25, which would add substantially more costs for the same value.
The 10% increase in SKUs has had a substantial effect, and we plan to further optimize our supply chain and inventory management, resulting in savings of up to $200 million.